- The Book of Why: The New Science of Cause and Effect (Pearl and Mackenzie, 2018)
- link to article "How a Pioneer of Machine Learning Became One of Its Sharpest Critics - The Atlantic"
- Causal inference is counterfactual
- Causal effect is a function of \((Y(1), Y(0))\)
- Causal inference requires estimation of unobserved reponses - it makes sense when the estimation does
- Causal inference requires assignment mechanism
- Assignment mechanism known in randomized studies; must be assumed or modeled in observational studies (propensity scores)
- Causal inference makes assumptions, e.g., non-interference, etc.